Dynamic Adaptive Distributed Computer System

ABSTRACT

A system, methods and apparatus are described involving the self-organizing dynamics of networks of distributed computers. The system is comprised of complex networks of databases. The system presents a novel database architecture called the distributed transformational spatio-temporal object relational (T-STOR) database management system (dbms). Data is continuously input, analyzed, organized, reorganized and used for specific commercial and industrial applications. The system uses intelligent mobile software agents in a multi-agent system in order to learn, anticipate, and adapt and to perform numerous functions, including search, analysis, collaboration, negotiation, decision making and structural transformation. The system links together numerous complex systems involving distributed networks to present a novel model for dynamic adaptive computing systems, which includes plasticity of collective behavior and self-organizing behavior in intelligent system structures.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/040,945, now U.S. patent Ser. No. ______, filed on Jan. 21, 2005,which claims the benefit of Provisional Patent Application Ser. No.60/539,095, filed on Jan. 23, 2004. Both applications are incorporatedby reference herein in their entirety.

BACKGROUND OF THE INVENTION

Simple databases were developed by IBM Corporation several decades ago.The relational database management system (dbms) was the dominantdatabase model until object databases were developed. Object relationaldatabases and distributed database systems are now the paradigm.Computer systems now use active storage in which the database is thecore of the system and microprocessors are simply embedded in the harddrives for database control and management. Advanced systems such as theU.C., Berkeley Telegraph Continuous Query (CQ) model of dynamic databaseorganization represent further developments of this database tradition.The main uses for these types of databases are data storage, dataupdating, data queries and data outputs. Oracle Corporation hasdeveloped database architectures that use temporal data by updatingknown temporal fields as the conditions of the data sets undergo change.Finally, the CHORO CHRONOS project in European research universities hassought to develop spatio-temporal databases that use and organizespatio-temporal data objects. Spatio-temporal data objects are complexdata sets represented in databases that change position across space andtime.

All of these dbms's are typically static in nature. Once they areprogrammed, data is input and output within a preset organizationalstructure. This model is useful for simple applications. But asmultitudinous data sources become ubiquitous, the limits of this staticmodel become obvious.

What is needed is a complex distributed dynamic database model that isadaptable, scalable and capable of evolution and reorganization. Ascomputer systems become linked in the next generation, this modeldistributed computer architecture will behave like an organic system innature. In fact, the biological theory of evolution is precisely themodel for complex collective self-organizing intelligent computersystems.

Whereas there have been numerous advances on small parts of computersystems, there has been relatively little progress involving themanagement, control, automation and synthesis of complex aspects of verylarge scale dynamic systems. The present system fills this importantgap.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention provide a system that optimizes theefficiency of a complex database. According to one aspect of theinvention, an adaptive dynamic computer system architecture having aplurality of system layers interconnected to one another is provided. Afirst layer includes a hardware system including microprocessors,application specific integrated circuits or continuously programmablefield programmable gate arrays. A second layer includes distributednodes. A third layer includes a distributed transformationalspatio-temporal object relational database management system. A fourthlayer includes a multi agent system of intelligent mobile softwareagents. A fifth layer includes plasticity behavior in intrasystemicinteraction. A sixth layer includes plasticity behavior in environmentalinteraction. A seventh layer includes a plurality of functionalapplications.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a list of system layers.

FIG. 2 is a schematic diagram of a distributed transformationalspatio-temporal object relational (D-T-STOR) dbms and multi-agent system(MAS) architecture.

FIG. 3 is a map of a D-T-STOR dbms and MAS with environmentaladaptation.

FIG. 4 is a map of a D-T-STOR dbms and intelligent mobile softwareagents (IMSAs) operation.

FIG. 5 is a schematic diagram of varied pathway frequency between T-STORnodes.

FIG. 6 is a flow chart of internal analytical operations that catalyze asystemic transformation.

FIG. 7 is a temporal diagram of an inter-node pathway in a process ofminimization.

FIG. 8 is a temporal diagram of network adaptation.

FIG. 9 is a temporal illustration of plasticity operation between nodeswith weaker links failing and stronger links gaining in intensity.

FIG. 10 is a temporal diagram of adaptation of the plasticity processthat catalyzes T-STOR transformation.

FIG. 11 is an illustration of connection of plasticity and D-T-STORtransformations.

FIG. 12 is an illustration of a hub of D-T-STOR databases connected to aconjoined hub of D-T-STOR databases.

FIG. 13 is a temporal illustration of a continuously programmable fieldprogrammable gate array (CP-FPGA) rewiring over phases for rerouting.

FIG. 14 is a flow chart of a distributed CP-FPGA sequential process.

FIG. 15 is a diagram of a D-T-STOR dbms.

FIG. 16 is a multi-phasal diagram illustrating the shifting of temporalpriorities.

FIG. 17 is a temporal illustration showing micro-change and macho-changeof categories.

FIG. 18 is a flow chart showing category transformation.

FIG. 19 is a diagram describing data repositioning process by temporalpriority.

FIG. 20 is an illustration describing evolution of an object in whichdata about the object is “pushed” to data storage.

FIG. 21 is a table showing the main classes of objects.

FIG. 22 is a multi-phasal illustration showing the reordering process ofobjects.

FIG. 23 is a multi-phasal diagram showing the repositioning of objectsbased on changing (temporal) priority.

FIG. 24 is a flow chart showing the T-STOR transformation process.

FIG. 25 is a flow chart showing the D-T-STOR transformation process.

FIG. 26 is a schematic diagram showing the process of splitting anobject to order into different storage locations.

FIG. 27 is a schematic diagram showing data tagging method fororganization of data flows in distributed T-STOR dbms.

FIG. 28 is an illustration showing the query of objects with object tagsthat change with priority.

FIG. 29 is an illustration describing the data tagging process as dataindices change with priorities.

FIG. 30 is a table showing data temporal priorities.

FIG. 31 is a temporal illustration describing a multi-phasal process oforganization of rivers of data in an active query process in a systemwith environmental interaction.

FIG. 32 is a flow chart illustrating a method of composite valuation ofcomplex data sets in distributed T-STOR dbms.

FIG. 33 is a temporal illustration describing spatial repositioning ofdata sets in a distributed T-STOR dbms.

FIG. 34 is a temporal diagram showing the disassemblage process andreassemblage process of data objects.

FIG. 35 is an illustration describing the sequential transformation ofdatabases in a distributed dbms.

FIG. 36 is a multi-phasal illustration showing the indices of objectsthat continually change revealing the varied positions of objects in adynamic distributed dbms.

FIG. 37 is a diagram showing the relationship between internal andexternal multi-agent systems.

FIG. 38 is a multi-phasal illustration showing a negotiation processbetween two software agents.

FIG. 39 is a temporal map illustrating analytical process of IMSA to useexperience (from environmental interactions) and new information to planscenarios according to limited range of probabilities.

FIG. 40 is a diagram describing a system that links factories, tradinghubs and supply chain management operations.

FIG. 41 is a diagram describing an enterprise resource planning system.

FIG. 42 is an illustration showing the structure of a global enterpriseresource management system (GERMS).

FIG. 43 is a four dimensional illustration of an object over time.

FIG. 44 is a four dimensional illustration of groups of tuples mapped ina T-STOR database over time.

FIG. 45 is a four dimensional illustration of four sets of points overtime represented in a T-STOR database, which react to changed movement.

FIG. 46 is a diagram of four 4-D T-STOR databases in a distributedcomputer system, each showing points over time.

FIG. 47 is a diagram of four 4-D T-STOR databases in a distributedcomputer system with points over time and their reactions for changedstructures.

DETAILED DESCRIPTION OF TIE INVENTION

Databases are a core component of computer systems that store, order andretrieve sets of data. They are the central instrument for the storage,search, analysis, organization and output of data sets and objects incomputer systems. When they are linked, databases become a key part ofdecentralized computer networks that can optimally organize data formaximum benefit. Complex large dynamic distributed databases are like ariverbed in the sense that they represent the foundations for data asthey are constantly input into the system over time, are analyzed andordered, and are, finally, output. Dynamic distributed databases,because they input and output data, constantly adapt their structure tochanging environmental inputs and the outputs that are required byspecific applications. Dynamic distributed databases can be active andseek out data from various sources. Similarly, they can anticipatechange, adapt and learn, and generate novel questions to which theyprovide answers. These complex dynamic distributed database systems alsochange their own structure as the overall network evolves. In otherwords, because the data inputs change within a changing environment, thedatabase architecture is designed so that the structure of the systemitself adapts in order to maximize performance and interact with theenvironment.

Dynamic distributed database systems employ a process which consists ofseveral phases. First, the initial system configuration acceptsdemand-initiated data sets from various sources. Second, the timesensitive data sets are organized efficiently by priority. Third, whenthresholds of efficiency are satisfied, a newly-implemented phaserestructures the architecture of the initial organization of the datastructures according to shifting priorities. This multi-phasal processcontinues as required, thereby leading to continuous restructuring ofthe architecture of the system as new data streams are input, triggersenable the transformation process, and data sets are output for variousapplications. The criteria for these changing states within the systemalso evolve. Because the system constantly evolves, it must anticipatechange within a limited horizon. The system is constantly optimizedwhile the data sets are constantly reorganized for maximum efficiencyand benefit.

Distributed T-STOR dbms

The core vehicle for the transformational process in the presentinvention is the transformational spatio-temporal object relational(T-STOR) database management system (dbms). These dynamic data storageentities consist of complex program code which inputs, stores, analyses,organizes, reorganizes, searches and outputs data sets. The chief modeof change in the dbms involves temporal transformation of the objects.The representations of the transforming objects in the database systemtake the form of data sets that continually reposition relative to theirchanging priority.

At a specific threshold point, the sum of data objects in the databasesystem initiates a process of transformation. This process begins withthe restructuring of the categories of the system. The database isanalogous to a warehouse full of goods that is constantly in a state ofchange and around which a large amount of unused goods, that haveaccumulated over the years, is scattered. At some point, it becomesnecessary to reorganize the goods in the warehouse. However, themanagers take the opportunity to create new efficiencies and begin theorganization process from the beginning by constructing new categoriesinto which the goods may be organized, because over the years the natureof the goods has changed. Another analogy would involve the need toreorganize a file storage system because new categories of business haveaffected the architecture of the categories of the accumulated files.Hence, the files need to be reordered, as do some of the file contentsthemselves. The contents need to be updated based on most recent data.Unlike the case of a file cabinet system, in the complex world ofdatabases, these updating systems occur very rapidly; the time sequencesof data restructuring are very short, and the data streams are vast.

T-STOR databases undergo transformational processes as they adapt theirstructures to novel data sets which are constantly reprioritized.Distributed T-STOR databases represent further implementations of thesystem in which data objects are sorted over a broader distributionscope. D-T-STOR databases create a complex computer network system inwhich database structures constantly transform in order to accommodaterapidly changing data objects in the environment. The creation of aD-T-STOR dbms has implications for the development of a dynamicdistributed computer system that has numerous novel and usefulapplications.

By developing interaction between various T-STOR databases, and byallowing interaction between the database system and the environment,the system allows for plasticity of system configurations and adaptationto a changing environment. On the system level, the process oftransformation allows for rewiring between database nodes andaccommodation to increasing and decreasing environmental activity. Thecontinuously changing transformational process of the D-T-STOR dbmsallows for environmental adaptation in real time.

In an additional embodiment of the system that facilitates dynamicdistributed computer adaptation, the system uses groups of continuouslyprogrammable field programmable gate arrays (CP-FPGAs). Continuallyadapting the programming of the hardwired structure of the circuits,multiple CP-FPGAs perform complex routing optimization tasks bysynchronizing the sequence of reorganization of circuits across adistributed network in order to accommodate a higher priority function.CP-FPGAs operate as advanced reprogrammable application-specificintegrated circuits (ASICs) which have the advantage of high performanceyet are constrained to a limited task. Reconfiguring the architecture ofthe CP-FPGAs in real time, allows them to constantly adapt to thechanging environment by instituting high performance functionalitywithout being constrained to a limited task.

In another embodiment, the hybrid combination of distributed CP-FPGAswith the D-T-STOR dbms creates a complex dynamic computer systemarchitecture which may more fully adapt to environmental changes in realtime. By combining the combinations of the rapid restructuring of theCP-FPGA hardware with the transformation processes of the D-T-STOR dbmswhich continually optimize temporal priorities, the invention is able toperform self-organizing processes which allow for real time interactionwith the environment.

Intelligent Mobile Software Agents

The main methods of inputting, ordering, searching, fetching andoutputting data sets in a dynamic distributed database system areutilized by intelligent mobile software agents (IMSAs). IMSAs aresophisticated software programs that can adapt, learn, generate orterminate code, move from machine to machine, and perform variousfunctions. IMSAs include search agents, analytical agents for datamining and pattern recognition, negotiation agents, collaboration agentsand decision making agents. IMSAs may use game theoretical modeling,simulations, and scenarios in order to perform a function or activate anapplication. The combination of multiple IMSAs in a dynamic distributeddatabase system signifies a multi-agent system (MAS). Teams of agentshave specialized (and multi-specialized) functions in the MAS of adynamic distributed database system. The present system is characterizedby a range of main operations and processes of the dynamic distributeddatabase system MAS.

IMSAs are capable of learning and prediction. IMSAs generateprobabilistic scenarios by employing fizzy logic and artificialintelligence techniques. IMSAs anticipate change in order to optimizesystem performance; thus potential future data sets are anticipated byanalysis of past data sets. Many of these complex data processes aretime sensitive. For instance, recent storage may be organized for easierearly retrieval, while older data sets are reordered with less priority.Analysis of environmental changes in recent data sets generates modelscenarios that involve anticipatory processes based on learning from andprojecting trends.

Prediction, Problem-Solving and Scenarios

One of the main challenges in developing automated dynamicself-organizing systems is the need to design adaptive and effectiveprocesses that anticipate behaviors. The ability to anticipate behaviorsor patterns in a system, depends upon the development of predictivecapabilities. Although predictions have constraints, the artificialcomputer systems field can overcome these constraints by borrowing fromthe field of econometrics and adopting designs based on Bayesianreasoning and other methods to predict and anticipate various scenarioswithin temporal limits. The present system contains methods for dealingwith the most recent data flows and data analysis to inform scenariogeneration and selection, based on the use of predictions andexpectations derived from the analysis of trends.

In some ways, the challenge for a complex dynamic system is one ofdiscerning how to solve problems. For every set of problems, a set ofsolutions is proposed and tested in real time. Prior patterns of problemsolving are presented in order to assess the optimal solution to a newset of facts. Solution option scenarios are anticipated by past problemsolving sequences. Anomalies are detected as limits in past solutions,multivariate analyses are performed on the problem, and a new set ofsolution options is generated and evaluated by combining possiblesolutions. Problem solving is performed on the fly in real time withlimited information. After a pattern of problems is recognized andsolution options are offered, the system will anticipate changes in theenvironment and generate simulated scenarios for optimal solutions tofuture problems. The analysis of trends and the generation andevaluation of scenarios suggest that the system is capable of leaningand adaptating to the uncertainty of ever changing environments. Thesesorts of models have been applied to securities markets but haveapplication to a much broader range of categories.

Induction and Learning

The application of prediction analysis and scenario generation andselection processes relies on principles of induction and learning.Consequently, the present system incorporates these processes.Artificial intelligence methods and techniques, including evolutionarycomputation and artificial neural networking are possible becausegenerations of programs have been trained to learn. Inductive inferencerepresents a way to learn from instances in the past, while deductiveinference stems from an axiomatic set of rules (and meta-rules) within afinite systemic range of actions. For the most part inductive inferenceis the dominant model learning in complex adaptive dynamicself-organizing systems.

The ability of an adaptive system to learn depends on a number offactors, including the environmental inputs, the analysis of patternsand trends, the development of experimental protocols, the assessmentand matching of potential solutions to real problems, the continualreadjustment process through periods of turbulence, the anticipation ofproblems and anomalies, and the generation, evaluation and selection ofsimulated scenarios and solutions.

System Architectural Self-Organization and Automatic Programming:Implementing AI

Because these systems are complex and dynamic, there is no equilibriumwithin them. Rather than simply passively analyzing and assessing datasets, the present system is active. It initiates actions and changes thestructure of the system itself in order to accommodate these changes. Inthis sense, the present system is characterized by plasticity within adynamic architecture in much the same way that the human brainconstantly rewires itself based on various threshold inputs andactivities. While the system constantly adapts itself to its changingenvironment and rewires itself, it is also a distributed network.Consequently, data streams flow between all active nodes in the system.Activity hubs emerge and decline. These data flows inform, and areconsequently rerouted by, the restructuration of the system.

In such a system, the network's computers themselves behave likeswitches in a giant distributed system. The benefit of this system'sdynamic reconfigurable unified artificial adaptive network is that asdemand changes rapidly, virtual intelligent hubs are created as needed.In this sense, the system self-organizes and suggests a sort of unifiedfield theory of dynamic distributed computation systems.

This system relies on a new generation of automatic programming. Thedistributed computer network contains software agents that control andorganize the broader network, with IMSAs that are capable of identifyingand assessing problems and generating, evaluating and selectingsolutions, all by generating program code autonomously.

Analogies to this complex metasystem may be found in both economic andbiological behaviors. In economics, the structure of markets constantlyevolves, driven by the behavior of self-interested agents. Inter-agentrivalry forces new market configurations. These intra-system processesreshape the architecture of the markets themselves, which then affectsthe competitive organization and so on.

In the context of biological systems, two main analogies are pertinentto the present system. First, evolutionary behavior resembles thecompetitive configuration of economic behavior Groups of individualscompete for limited resources as whole species rise and fall accordingto environmental circumstances. These complex processes have led to suchdiverse phenomena as collective behavior in groups of animals herding,school flocking and swarming) and the organization of antibodies in thebloodstream to fight off viruses.

The second analogy between biology and complex self-organizing systemsinvolves genetics. Refined over millions of years, genetic material isknown to be an amazingly complex self-organizing system. Specific genesare activated at specific times to perform other functions, forinstance, to generate a protein which in turn will activate other genesto perform a function within a limited time. This complex dance ofgenetic material, and its mutations over time, allows us to survive inand adapt to hostile, uncertain and changing environments.

The present system is designed to be an artificial distributed,adaptive, self-organizing, auto-programming computer system that, likegenetic material performs various complex functions. In fact, it is asystem within a system because is employs a MAS within the distributedcomputer network. Such a system is not only multi-tasking, but adaptive,as inputs are evaluated and solutions generated to solve constantproblems presented by a demanding and changing environment. Finally, thesystem constantly reconfigures its architecture in order to optimize itssolutions. The system uses AI techniques and methods, includingevolutionary computation, artificial neural networks, Bayesian reasoningand fuzzy logic, in order to meet various challenges, from analysis ofproblems to the generation and selection of simulated scenario options.

Linkages

One of the key aspects of the system is that it links subsystems. Inthis sense, the system is a “metasystem” that controls various networks.The scope of this metasystem is broad. It is able to link computernetworks from the following categories: commerce (commercial hubs,demand-based negotiation and transactions and supply chain management),financial networks, traffic routing, information organizationmanagement, demand-based learning, data mining and analysis, (mobile)sensor networks, simulation modeling, collective robotics, wirelessmobile communications, automated decision making and adaptive computersystems.

These complex systems share two main attributes. First, they are alladaptive dynamic systems that use self-organization of data inputs thatdepend on changing and unpredictable environments. Second, thesenetworks can be linked into one system for creation of a single organicmetasystem.

The limits of static computer networks make it necessary to posit a morerealistic system that emulates the dynamism and unpredictability ofcomplex systems. These advanced systems require novel learningmechanisms that adapt and optimize their evolutionary development paths.The present system model satisfies the requirements of an evolutionarydynamic self-organizing and adaptive network.

The present system describes connections between software and hardwareon the one hand, and middleware and its specific applications on theother.

Applications

Dynamic traffic routing optimization is made more efficient with thepresent invention because it uses CP-FPGAs and the D-T-STOR dbms togreater effect than earlier systems. Communications resources are moreoptimally routed with the present invention. Computation resourcemanagement is also optimized as the system's procedures implementplasticity operations that maximize resources; increasingly active nodesare empowered with greater capacity while less active nodes aredisempowered so as to shift resources. The transformational capabilitiesof the present system allow for the constant prioritization not only ofdata objects to their optimal routing, but also of whole hubs of variedactivity. Taken together, the various routing optimization systems willallow a ubiquitous computing platform which continuously adapts to itsusers and its environment. With increasingly efficient and usefuldynamic sensor networks, this system is the type upon which our securitywill depend in an increasingly threatening world.

The present invention allows for real time simulation analysis anddynamic scenario analysis because it accommodates real time data inputs,prioritization of data, organization and reorganization of data sets,learning and anticipation. Applications of the present invention tocommercial processes, range from the organization of a city toorganization of commercial and trading hubs, supply chain management andenterprise resource management. From a demand-based commercial system ofretail or wholesale acquisition on the Internet to the creation andself-organization of adaptive commodity trading hubs, the presentinvention is useful. The system is of particular use in the developmentof an optimized dynamic supply chain management system which will adaptto the rapidly changing environment. The system is also useful appliedto dynamic proximity marketing system which allows consumers to besolicited while walking through a mall during different seasons suchthat spending their priorities and preferences adjust to changing fadsof taste.

The present system is also useful for dynamic enterprise resourceplanning and management systems. On a world-wide scale, the invention isuseful for the global enterprise resource management system (GERMS) inallowing large corporations to manage and link large adaptive systemsthat interact with a changing environment.

The present invention is very useful in dynamic distributed collectiverobotics networks that must interact with rapidly changing environments.When the present system is integrated into collective robotic systems,the adaptation process will make possible group automated behaviorshitherto limited to biological organisms.

Finally, the present invention allows the linking of various computersystems in a metasystem.

Problems that the System Solves

The system provides solutions to a number of problematical questions.How does one link a range of complex functions in a distributed network?How does one integrate a MAS into a complex distributed computernetwork? How can learning processes be structured for the constantadaptation needed by the system? How can the various parts of the systembe optimized to work together seamlessly? How can disparate functions belinked, from commercial and financial systems to information andlearning systems and from collective robotics systems to wireless andtraffic network systems? How can analytical functions be integrated withactive functions in a complex distributed computer network?

The system solves a range of important problems involving computersystem management. Regarding distributed storage capabilities, if onenode is unusable, the current system allows the whole system to beuseful because it constantly reorganizes. The D-T-STOR dbms allows afail-safe mechanism for restructuration around limited or decayingnodes. In wireless or in military failsafe communications systems, thismodel is particularly useful.

In another sense, this invention allows the distributed computer systemto continually optimize connections between active nodes, which isuseful in adaptive routing architectures involving communications andcomputation resource systems.

The present invention also allows resources to be allocated to the mostefficient uses, by accommodating shifting priorities in real time.

Finally, the present invention is designed as a novel self-organizingsystem that adapts to environmental interactions in real time. By usinganticipatory behaviors, learning, and automated programming features,the interaction processes are maximized for mission criticalapplications.

Advantages of the System

The present invention has numerous advantages over earlier models. Thesystem represents a way to link multiple networks for maximumefficiency. The system optimizes the adaptive self-organizing operationsof dynamic networks. The system is applicable to a broad range ofapplications, from mobile computing network optimization to mobilecollective robotics and from dynamic commercial systems to remotesensing networks.

Transformability of the T-STOR database architecture allows the computersystem to adapt to new environmental conditions. Re-transformations ofthe T-STOR dbms allow for continuous adaptations to rapidly changingenvironments.

The use of spatio-temporal objects in the T-STOR database structureallows for the organization of object categories that most accuratelyreflect reality.

The use of distributed T-STOR databases allows a range of usefulapplications. The D-T-STOR dbms allows for the organization,reorganization and automated self-organization of complex processesacross space and time. The use of D-T-STOR databases in the plasticityof operations is a novel advantage of the present invention. Theconstant transformability of multiple databases allows the operation ofa complex computer system that may directly interact with theenvironment in real time.

Endowed with the D-T-STOR dbms and the IMSA network, the distributedcomputer system identifies new paradigms and transforms to a newparadigm at key thresholds in real time so as to maintain systemdynamism and efficiency. By preserving limited resources, the presentinvention is able to do more with less computation capacity.

The use of CP-FPGAs allows for a distributed system that can optimizerouting processes. When combined with the CP-FPGA hardware, the D-T-STORdbms becomes an extremely powerful and highly responsive self-organizingsystem that interacts organically with complex environmental processes.

The D-T-STOR dbms is contrasted with static database systems that behaveas large storage systems for complex data inputs and data miningprocesses.

References to the remaining portions of the specification, including thedrawings and claims, will explicate other features and advantages of thepresent invention. Further features and advantages of the presentinvention, as well as the structure and operation of various embodimentsof the invention, are described in detail below with respect toaccompanying drawings, like reference numbers indicate identical orfunctionally similar elements. Since the present invention has numerousembodiments, it is not the intention herein to restrict the descriptionof the invention to a single embodiment.

The system and methods incorporated in the present invention areimplemented by using software program code applied to networks ofcomputers. Specifically, the present invention represents a dynamicadaptive distributed computer system that includes a multi-agent system(MAS). The main embodiment of the distributed computer system isimplemented with complex databases. An additional embodiment of thedistributed computer system is implemented with continuouslyprogrammable field programmable gate array (CP-FPGA) integratedcircuits. Whether the main components are primarily constructed ofhardware or software, or both, the system incorporates intelligentmobile software agents (IMSAs) within the MAS that organize into groupsfor problem-solving functions.

A function of the system is to optimize efficiency of a complex databasesystem by continuously reorganizing the system to adapt to changingenvironmental inputs. The system uses transformational spatio-temporalobject relational (T-STOR) databases which continually transform inorder to maximize efficiencies while processing large amounts of datainputs and outputs. Distributed T-STOR databases organize a number ofnodes in a network. Objects are complex data sets that change in spaceand time. The D-T-STOR database management system (dbms) is constructedto efficiently order and process complex objects.

The detailed description of the drawings is divided into several partsthat explain: (1) The overall system for linking the D-T-STOR dbms withthe MAS which manages IMSAs, (2) the process of self-organization ofnetwork plasticity that the adaptation of the T-STOR dbms makespossible, (3) the hardware system of continuously programmable FPGAsthat implements the system, (4) the T-STOR database, (5) a distributedT-STOR dbms, (6) an IMSA collective interoperation in the MAS, and (7)various applications of the system.

General Architecture and Dynamics

FIG. 1 illustrates the layers of the dynamic adaptive distributed systemarchitecture. The first level shows the hardware for the system. Thishardware consists of microprocessors that employ the traditional vonNeumann architecture for fetching programming code from memory. In anadditional embodiment, integrated circuits (ASICs) are hard wired forincreased efficiency in specific applications. In still anotherembodiment, continuously programmable field programmable gate arrays(CP-FPGAs) benefit from the advantages of both the microprocessor modeland the ASIC model by continually restructuring their gate architecturesto achieve maximum efficiency for particular tasks.

In the second level, these main computer hardware models are structuredin multiple distributed computer nodes. These may be local area network(LAN) or wide area network (WAN) configurations. In general, thesedistributed computer nodes resemble the GRID computing system.

In the third level, the distributed transformational spatio-temporalobject relational (D-T-STOR) database management system (dbms) isorganized to be the software platform through which object data setsinteroperate. T-STOR databases are present in each of the hardwarenodes.

The fourth level consists of the multi-agent system (MAS) within whichoperates the intelligent mobile software agent (IMSA) collective. TheIMSAs use complex software program code to execute specific instructionswhich perform specific functions. By employing hybrid evolutionaryprogramming techniques and tapping substantial computational resources,IMSAs are capable of automatic programming processes.

On level five the plasticity of intrasystemic self-organizing behavioris evidenced. That is, within the MAS alone, analytic (as distinguishedfrom empirical) decisions are made.

By developing an architecture for the self-organization of complexoperations involving interaction with all adaptation to uncertain andchanging environments, level six operations order complex plasticitybehaviors to extrasystemic interactions.

On level seven a range of functional applications occurs. Theseapplications include: computational resource management andcommunications resource management applied to optimal routing processes;automated commercial systems; supply chain management systems; globalenterprise resource management systems; collective robotic systems; realtime simulation and scenario analyses; dynamic sensor networks; dynamicproximity marketing systems; advanced security systems; bioinformaticssystems; advanced ubiquitous computing systems; and interoperation ofthese various systems.

FIG. 2 shows a cluster of T-STOR databases. T-STOR databases 1 (230), 2(240), 3 (260), 4 (270) and 5 (250) are connected to each other as nodesin a distributed system. The MAS is integrated in the software of thedistributed cluster.

FIG. 3 illustrates the database nodes and the MAS. In this figure,environmental inputs (at 300 and 335) interact with the MAS.

FIG. 4 shows intelligent mobile software agents (IMSAs) interoperatingwithin a distributed T-STOR dbms. In this example, IMSA 1 (at 430) ismoving into position between databases (at 440). IMSA 2 (at 460) ismoving from T-STOR 2 to T-STOR 3 (470). IMSA 3 (at 490) is moving toT-STOR 4 (485), while IMSA 4 (at 480) is moving to T-STOR 2 (450).

The flexible activity between T-STOR databases is illustrated in FIG. 5.The activity between 500 and 530 is most intense, as shown in the numberof dotted lines. The activity between 500 and 520 is the next mostintense, while the activity between 500 and 540 and between 520 and 540is successively less intense. The activity between 530 and 540 is lessintense yet, while the activity between 520 and 530 is the least intenseof the nodes shown here. These relative frequency factors reveal,significantly, the changing intensity between relative connections inthe distributed system, and this process, as it changes, illustrates theplasticity of systemic adaptation.

This transformative process is briefly illustrated in FIG. 6. At 600,the initial program parameters are activated. The internal MAS analysisdetermines the need to modify program parameters (at 610), which arethen adapted (at 620). The internal analytical operations of the MAScatalyze the transformation of the system (630) and the adapted programparameters trigger change in the system configuration (640) whichfinally transforms the system (650).

FIG. 7 shows the simple process of two nodes decreasing frequencyintensity over time.

Taken together, these simple processes of plasticity and transformationlead to decreasingly intense and inevitably inactive nodes over time,illustrated in FIG. 8. In the first phase, the increasingly active nodeconnections, namely those between T-STOR 1 (800), T-STOR 2 (810) andT-STOR 3 (820), maintain connections while the connections between thesenodes and T-STOR 4 (830) become inactive. One sees in the second phasethe clear inactivity of T-STOR 4 (890), while the addition of T-STOR 5(850) and T-STOR 6 (870) creates an increasingly active nodedistribution system. This process illustrates the plasticity modelwithin a transformative distributed network. With nodes coming on lineas they become active and inactive nodes falling away, the systemcontinually adapts.

FIG. 9 shows this plasticity operation whereby nodes with active linksbecome stronger and solidify while weakening others decay and fall away.In illustration 9A, the link between node 910 and node 916 becomes weakas node 916 falls away. Similarly, the links between nodes 905 and 908are weak. FIG. 9B, node 924 becomes weak, and the link between 924 and926 falls away. In the meantime, at this same phase in the system, node940, though weak, is added and linked to 938. Finally, at the phaseshown in FIG. 9C, node 924 is removed, while node 964 strengthens, asdoes the link between 924 and 964. The link between 952 and 956 is alsostronger, showing that the nodes are not tenuous. The transformationfrom the configuration in 9A to the configuration in 9C in thethree-part process reveals a clear change in the network suture.

In FIG. 10 a similar network transformation process is illustrated. Asshown in phase 1, the system begins with initial inputs (at 1000, 1015,1030 and 1045) that lead to the analytical stage (at 1005, 1020 and1035) and the initial database storage (1010, 1025 and 1040), whichrepresents the output stage in this example. However, the decliningfrequency of inputs at 1030 and 1045 leads to a decline in activity at1035, which leads to a decline at 1040. This in turn leads to a declineof the entire web of connections illustrated in the box in phase II,with 1085, 1090, 1189 and 1095 in decline and inactive. The remainingsection of the network is intact. The inputs (1050 and 1065) change,leading to the analysis stage (1055 and 1070) and new database storageconfigurations (at 1060 and 1080). This example illustrates a simpletransformative process utilizing distributive network plasticityprocesses which adapt the system to changing inputs.

This transformative process is also described in FIG. 11. The dottedovals on the top right side represent the declining inputs which changethe network configuration. This decline in the upper right section ofthe network shifts emphasis to the active side on the left. As theactive network information is input into the T-STOR databases (1150 and1155 and declining database 1160), these databases transform theirstructure to improve their performance. Similarly, the databases on thebottom layer of this figure (1165, 1170, 1175 and 1180) are transformedinto data inputs from 1150 and 1155 within the distributed networkchange.

FIG. 12 shows a conjoined hub of T-STOR databases linked to a T-STORhub. These database network structures constantly change.

While the distributed T-STOR dbms represents a model for a networksystem transformation process, another model, implemented in hardware,can be observed in the use of distributed CP-FPGAs, which illustrates asimilar system process with network plasticity. Designed to change thestructure of hardware in the field, an FPGA is an application specificintegrated circuit (ASIC) that can be rewired ASICs have advantages thatmicroprocessors lack; since microprocessors require the generation ofnew program code from memory to process specific instructions, there isa speed-of-operation disadvantage in completing tasks. On the otherhand, an ASIC, though designed to perform a single function rapidly, islimited to a specific task, which similarly restricts its robustness.FPGAs are ASICs that periodically change their structure by rewiring tonew configuration ASICs. While the time it takes to rewire is anintermediate disadvantage, FPGAs more than make up for this intervalprocess lag by maintaining the ASIC performance advantages. CP-FPGAs arecontinuously reprogrammable versions of simpler FPGAs. CP-FPGAs enablecomputers to continuously rewire as the environment changes, therebyallowing for optimal routing processes.

The present invention uses multiple distributed CP-FPGAs in clustersthat may constantly change their structures in order to accommodatespecific adaptive features. In FIG. 13, a multi-phasal representation,Phase I shows the relationships between gates (boxes). The connectionsare numbered in sequence. In this case, the transistors are turned on inorder from 1300 to 1305, then 1310, 1325, 1340, 1335, 1130, 1315 andfinally 1320. In Phase II, the order of connections is changed. In thissecond case, the transistors are turned on from 1350 to 1355, then 1370,1375, 1390, 1385, 1380 and finally 1365. In this example, 1360 isrendered inactive. This may be because the system is seeking to maximizeits efficiency or because 1360 is damaged and the system is therebyrequiring a reroute of its pathways.

The use of multiple CP-FPGAs in a distributed system is vital to anadaptive system. Whereas most research has been focused on individual orspecific FPGAs or CP-FPGAs, their valuable use in distributed systemsneeds to be further explored. The value of rewiring the architecture ofmultiple CP-FPGAs may be observed in the example of a dynamic systemwhich requires adaptation to its dynamic routing optimizationprocedures. FIG. 14 describes this process.

In FIG. 14, routine routing of traffic is in equilibrium (1400) at theinitial point of the restructuring process. An analogy to this processmight be normal rush-hour traffic in a typical city. However, if anemergency occurs, a high priority entity would need to be routed quickly(1405). In the analogy, a fire truck must take priority over ordinarytraffic and its routing sequences. CP-FPGA #1 re-orders from a specificASIC configuration to another specific ASIC configuration (1410) at akey time to more efficiently reroute the system and enable the higherpriority function. In the case of the fire truck, the first trafficlight would be changed to accommodate the fire truck's movement and giverelatively lower priority to ordinary traffic. The second intersectionalso gives the truck priority over other traffic. CP-FPGA #2 thenre-orders from one specific ASIC configuration to another (1415) inorder to more efficiently perform the rerouting task of granting higherpriority to the one entity. This process does not involve waiting untilthe last minute but rather allows the CP-FPGAs to continuouslyrestructure their functions in a sequence that optimizes the processingof the distributed network. Information is forwarded to CP-FPGAs whichprompts them to change their structures to preserve the optimaldistribute system priority sequencing. Once the emergency concludes, thefirst CP-FPGA may revert to the original configuration in order toreaccommodate ordinary traffic flows. Other CP-FPGAs reconfigure tocustom or ordinary configurations (1420) depending on overalldistributed system demand. The fire truck is not slowed by the trafficrouting system, because it is given the highest priority, while theother traffic flows are organized to minimize delays as the systemcontinuously reorganizes. The routing between CP-FPGAs optimizes routingfor emergencies or high priority demands even at peak traffic times(1425). When the emergency passes, the CP-FPGAs return to the routineconfiguration of normal routing (1430).

Use of the distributed CP-FPGA network model represents anotherembodiment of the dynamic adaptive distributed system implemented inhardware rather than software. This unique model sets forth a hard wireapproach to system adaptivity and plasticity which may far increaseproductivity and efficiency of the overall system. Another embodimentyet merges hardware and software plasticity models into a complex hybridmodel. This hybrid approach combines the high performance of thehardware with the advantages of transformability of the D-T-STOR dbms.

FIG. 15 illustrates a distributed network configuration of T-STORdatabases in which sensor data is input into each network node. Theimportation of external data sources provides a dynamic stimulus to theoperation of the system.

FIG. 16 shows the multi-phasal process of shifting temporal priorities.In Phase I, at the far left of the illustration, data flows are input at1600 and 1620 at the lowest priority. However, as time continues, atpositions 1605 and 1625 the priorities increase. Similarly, the mostrecent temporal events at 1610 and 1630 further increase in priorityuntil the highest priority is reached at 1615 and 1635 at the mostrecent time threshold. Temporal dynamics are critical to the plasticityof adaptive systems.

In a static system, categories for storing and organizing data arepre-established and unchanging. In contrast, category structures in theT-STOR database system are dynamic and adaptive. FIG. 17 shows the macrochanges over five phases of a category as it grows from phase one (1700)through phases two (1705), three (1710), and four (1715). However,because of its relatively fast growth and large size, the category isrestructured into two smaller categories (1720 and 1725). On the otherhand, the micro changes of a category, shown on the right side of theillustration, are evolutionary, posing relatively little need to splitthe category into two or more. Category adaptations are an importantpart of the T-STOR transformation process because through such changesrestructuration processes are performed at specific thresholds. Thisphenomenon is shown in FIG. 18.

In FIG. 18, database categories grow to reflect environmental change(1800). The categories reach a threshold (1805), as in FIG. 17 at 1715,and the category splits into at least two new categories (1810). Afterthe creation of two new categories, data from the original category issub-categorized by topic (1815) and sorted into the newly createdcategories (1820). This process is similar to cell-division inbiological systems, though in the present system the content of thecategories is redistributed. The system undergoes a transformation andself-organizes into a new configuration.

FIG. 19 shows the data repositioning process by temporal priority in aT-STOR database system. Multiple data sources (1900 and 1905) are inputinto the system via the interface (1907). The inputs are presented tothe data router (1910) which inputs the data sets, typically objectswhich represent adaptive data objects, into the dynamic data storageregion (1980). In this example, data is organized by temporal priorityfrom the highest data priority (1930) to the lowest data priority(1970). As the environment changes and temporal priorities change, thedynamic data storage constantly reorders data inputs. Data is output bypassing data sets to the data retriever (1920) which is accessed via theinterface.

The temporal process of inputting data sets related to an object isillustrated in FIG. 20. In this illustration, an object is in motionfrom position 2000 to 2040. Data about the object also change. Forexample, if its position and composition change, data about thesechanges are input into the system in real time. These data sets areinput into the T-STOR dbms (2050).

Objects can be classified into several categories, as seen in the tableof FIG. 21. Data sets (2100) and mathematical objects (2105) areconstrued to be abstract data. Spatial data are either two dimensional(2110) or three dimensional (2115). Finally, temporal data arerepresented as one dimensional plus time (2120), two dimensional plustime (2125), three dimensional plus time (4D) (2130) and multimedia(2D+time or 3D+time plus additional sense data such as sound) (2135).

Data sets are constantly reordering in our environment as objects andevents change position over time. It is possible to represent data setsas objects which change in time and space. The relationships of objectsto each other also change. Consequently it is important to reorderobjects as computational representations. FIG. 22 illustrates amultiphasal example of the reordering process of objects.

In Phase I, objects are input into the data pipeline in a consecutivesequence, with object 4 in first place (2205), object 3 in second place(2220), object 1 in third place (2210), object 6 in fifth place (2215)and object two in sixth place (2230). Phase II shows that downstreamthis order is reorganized along new priorities at a different time. Asthe configuration of the pipeline narrows, the objects are reorderedinto the sequence presented in Phase II. The third phase configurationrestructures the organization of objects from the second phase butmaintains their relative positions.

The relevance of object organization to the T-STOR database system isshown in FIG. 23. In Phase I of this illustration, objects are organizedby priority, with those at the top left of highest priority and those atbottom right of lowest priority in the transformational database. Thus,object 173 at 2310 has the highest priority, object 174 at 2315 hassecond highest priority, object 175 at 2320, third highest priority, andobject 176 at 2325, the lowest priority illustrated here.

In Phase II of FIG. 23, the positions change. Because the T-STOR dbms istemporal, priorities have changed over time. The transformation hasaccorded object 175 the highest priority at 2335, while object 174 hasdropped position to 2340. Object 176, which has not changed position at2345 has nevertheless changed priority relative to the other objects inthe system. Object 173 has radically changed priority at 2350 and hasfallen far behind relative to the other objects.

FIG. 24 is a flow chart that demonstrates the T-STOR transformationprocess. After the T-STOR database categories restructure into a newtypology of categories (2400), data are ordered by topic (2410). Thedatabase is reordered into the new category typology according totopical priority (2420). As data are input, the highest (temporal)priority data are reordered (2430). Because data are constantly inputand reordered by changing criteria, the T-STOR database is in continualdisequilibrium (2440).

Because the D-T-STOR dbms is distributed, data are organized acrossvarious nodes. In the same way that a single T-STOR databasereorganizes, the D-T-STOR system reorganizes (spatio-temporal) data butdoes so across spatial position. FIG. 25 is a flow chart that shows thedistributed T-STOR transformation process. In the case of two or moreT-STOR databases in this example, the databases restructure into a newtypology of categories (2500), and data are ordered by topic (2510).Data are reordered into the new category typology according to topicalpriority (2520) across databases. Data in databases are then ordered bycontinuously changing (temporal) priority (2530), and the D-T-STORdatabases are in continual disequilibrium as they constantly restructure(2540).

FIG. 26 shows the process of splitting an object so as to direct it intodifferent storage locations. At 2600 a block consists of a grid withspecified positions at A1, A2, A3, A4, B1, B2, B3 and B4, an object isrepresented by the sum of the parts contained within the grid markings.Each part of the block marked by a section in the grid is thenrepositioned into various locations at 2610, 2620, 2630 and 2640. Thesepositions may occur within a single database or among multiple databasesin the D-T-STOR dbms, though in the present embodiment they arerepresented within several separate databases. In this example, A1, A2,A3 and A4 are equally distributed sequentially across the fourlocations. However, B1 is in db2, B2 in db4, B3 in db1 and B4 in db3.This phase of the process is a snapshot of a complex process ofcontinual repositioning across different locations. To keep track ofthis complex process of repositioning of objects into the D-T-STOR dbms,it is necessary to tag data sets. This is shown in FIG. 27.

In FIG. 27, a data tagging method is shown for organizing data flows inthe D-T-STOR dbms. In this case, tree kinds of objects (2700, 2710 and2720) are organized into sections, with each section containing aspecific number. Representations of each object tag are then organizedacross several databases, with the first object (which consists of tagsrepresented by numbers 1, 2, 3 and 4) being distributed across the fourdatabases in the sequence of 1, 2, 3 and 4. Similarly, the second object(which consists of tags represented by numbers 5, 6, 7 and 8) and thethird object (which consists of tags represented by numbers 9, 10, 11and 12) are also distributed across the four databases in the sequenceof 1, 2, 3 and 4.

The D-T-STOR dbms queries objects by accessing their referring datatags. FIG. 28, a single T-STOR database is illustrated with six levels.The first level is the highest priority level and the sixth level is thelowest priority level. Object tags are identified by priority andretrieved in order. The objects and the tags to which they refer are inconstant motion over time, reflecting their changing (temporal)priorities. At a point of long-term inactivity, the lowest priorityobjects (2820) are removed from the active system to long-term datastorage (2840) where they are preserved, thereby freeing up data storagecapability for the active system. In this figure, objects are queried(2800) and tags are located at position 7, position 173 and position205.

FIG. 29 captures the data flow process within a single T-STOR databasein which multiple data sources are input (2900), data are evaluated bypriority (2910) and data are directed to data storage by priority(2920). Data objects are then tagged (2930) and indexed (2940). Giventhe dynamic nature of the data flows, with the data priorities thatchange over time and their subsequent repositioning, data indexes thatrefer to the data tags (2950) constantly change. In fact, the best wayto track the changing priorities and locations of the data tags (whichrefer to the changing data objects) is to access the changing indexes(adaptive hash tables) that refer to the tags.

FIG. 30 shows the relative prioritization of data objects (and theirtags) from highest priority (3000) to lowest priority (3050) inconsecutive sequence.

The overall system is shown in FIG. 31. In this illustration, rivers ofdata are input into a single database at 3140. The data sets are theresult of interaction with a changing environment. As new data are inputinto the database, the database continually reorganizes at position two(3145), position three (3150) and position four (3155). Constantinteraction with the environment, generates active queries (3135) forcontinually modifying (reprioritizing) data sets. Episodic queries(3110) occur at different phases (3115, 3120, 3125 and 3130) of thesystem that reflect access to the system via human-computer interface.These queries reflect brief snapshots of data from the transformationprocess.

In order for objects to be organized, prioritized, tagged and queried,they need to be evaluated. The flow chart in FIG. 32 shows this processof composite valuation of complex data sets in a D-T-STOR dbms.

In FIG. 32, the temporal evolution of an object is tracked (3200) anddata sets are evaluated for priority (3210) and tagged. Data sets areorganized into categories (3220), and the categories are structured,ordered in relation to each other and given priorities (3230). Asupplementary inventory of categories of object attributes is developed(3240) as the objects develop new attributes that are not captured bythe initial category schemata. When new object aspects emerge and newcategory aspects develop to mirror the new object attributes, categoriesare split and reordered (3250). A new library of object attributes isresorted to fit into the new categories (3270). As the overallenvironment changes, the prioritization and representation of objectsand object attributes change in the D-T-STOR dbms (3280).

FIG. 33 shows the spatial repositioning of data sets in a D-T-STOR dbmsover time. In Phase I, the object attributes are organized in the gridat 3300. The attributes in this example are then divided into A1, A3,A6, B2, B4 and B5 at 3310 and A2, A4, A5, B1, B3 and B6 at 3320. Theinitial division of the object involves two databases. In Phase II ofthis process, the object attributes are further reordered into fourdatabases. A1, A3 and B3 (3330) are stored in the first database. A5 andB5 (3340) are stored in the second database. A1, B2 and B6 (3350) arestored in the third database. Finally, A4, A6, B1 and B4 (3360) arestored in the fourth database. Distributing object attributes acrossmultiple databases is useful in order to avoid system outages. Bycontinuously redistributing the objects across multiple databases, thedatabase system remains dynamic. Such dynamic data processing allows thesystem to optimally route and store complex data sets.

Data sets of object and object attributes are disassembled andreassembled in the D-T-STOR dbms. In Phase I of FIG. 34, two objects ina database at 3400, AB (3410) and CD (3415), are disassembled into A(3425), B (3430), C (3435) and D (3440) at 3420. At a later time, thesedisassembled data sets are reassembled at 3450 as BC (3455) and AD(3460). Finally, in this example, at 3470, these data sets aredisassembled and redistributed into B (3475), C (3480), A (3485) and D(3490).

FIG. 35 illustrates the general transformation process of severaldatabases in a D-T-STOR system with the arrows inside the figuresshowing the reordering process within each database.

In FIG. 36, the indices of objects that continually change are revealedand the varied positions of objects are shown in a dynamic D-T-STORdbms. Phase I objects at position 124, position 258 and position 735(3600) are transformed in Phase II (3610), to position 178, 316 and 963respectively. In Phase III, the same objects are transformed to position215, 374 and 978. Finally, in this example, the same objects are furthertransformed in Phase IV to position 361, 523 and 994 respectively. Thisexample shows a transformation based on the diminishing (temporal)prioritization of objects as they move through the database system.

FIG. 37 shows the relationship between the internal multi-agent system(3730) and the external multi-agent system (3720) as it interacts withthe environment. The database system stores data sets that representobjects in the world. As these objects change in the environment, theirrepresentations change in the storage system. The two multi-agentsystems interact as the external MAS inputs empirical data sets whilethe internal MAS functions strictly analytical.

The overall MAS that encompasses the external data inputs and theinternal data analysis consists of intelligent mobile software agents(IMSAs) that collaborate, cooperate, negotiate and make decisions aboutcollective behaviors. In FIG. 38, two IMSAs, A and B, negotiate in phaseone at 3800 and 3810, in phase two at 3820 and 3830 and in phase threeat 3840 and 3850, when an agreement for an action is reached at 3860.

In Phase I of FIG. 39 at 3900, the latest information about events ismerged with experience about the class of events. At the next phase, at3910, more information is added to the existing pool of experience. AtPhase III, the latest information is added to the system and a scheme isproposed, at 3920, to solve the problem at the horizon of the evolvingpresent Future prospective scenarios are mapped for the probable successof the solution within various ranges of 20% (3930), 40% (3940) and 80%(3950). This model emulates the Bayesian learning model in that an IMSAmay analyze the prospective success of specific schemes as the latestinformation is added to the sum of information used to analyze aproblem.

Of the numerous applications of the present invention, FIG. 40illustrates the system as it links factories (4010) to natural resources(4000), trading hubs (4020), a shipper (4030), importer (4040),distributor (4050), retailers (4060) and consumers (4070). The inventionis additionally useful for organizing information in the supply chainmanagement system that links each entity in the distribution system.

Similarly, in FIG. 41, rings of sub-assembly manufacturing plants (4120)are organized around an assembly plant to provide the most efficientJust-in-Time (JIT) processing of commodities (4100) to meet consumerdemand (4140) in real time. Use of the invention reduces the time fromrealization of demand to supply of products to a minimum. The inventionis therefore expedient for enterprise resource planning systems.

Global corporations will be able to organize their operations moreefficiently with the present invention. FIG. 42 illustrates a globalenterprise resource management system (GERMS). A business's mines (4200)are linked to its factories (4210) which are linked to its bank (4225),shippers (4220), importers (4230), distributors (4240) and retailoutlets (4250) which in turn are linked to the consumers (4260). Theincreasingly complex global system of each business is dynamic andrequires the type of increasingly adaptive and efficient managementsystem that the invention embodies.

FIG. 43 is a multi-dimensional illustration of a three dimensionalobject over time. The object's initial parameters are delineated at thebounded points between A (4300), B (4305), C (4310) and D (4315).However the diagram shows the movement of the object over time to newcoordinates which represent the object at bounded points between A′(4320), B′ (4325), C′ (4330) and D′ (4335).

FIG. 44 is a multi-dimensional illustration of representations of groupsof three dimensional objects moving through space and time. The threeobjects, 1 at 4400, 2 at 4410 and 3 at 4420 are represented as threedimensional groups of tuples mapped into a database at a specific pointin time, but the three objects are in motion and change position at alater temporal moment to 1′ at 4425, 2′ at 4430 and 3′ at 4435. Sincethe objects may transform their identity as well as their position, thetuples which change temporal and spatial position and which refer to thesame object may not refer to the precisely identical initial object.

FIG. 45 is a multi-dimensional illustration of an object as it movesthrough a database. The main object is referenced at A (4500). However,the movement of the data reflected by the moving object affects otherdata sets in the database. As the object changes position, other objectrepresentations in the database, at B (4510), C (4520) and D (4530),also change positions. This view illustrates the transformation aspectsof a T-STOR database.

FIG. 46 is a multi-dimensional illustration showing objectrepresentations of objects moving through four databases in adistributed T-STOR dbms. In database A, an object is represented at4600; while in database B an object is represented at 4610; at databaseC an object is represented at 4620; and at database D an object isrepresented at 4630.

FIG. 47 is a multi-dimensional illustration of multiple objects as theymove through a D-T-STOR dbms. In database A, the object is representedat 4705, while the reactions of moving objects to its changing positionsare shown at 4700 and 4710. In database B, an object is represented atchanging positions at 4720 and the reaction of objects are tracked at4715 and 4725. In database C, an object is represented in changingpositions at trajectory 4735, with the reaction of objects representedat 4730 and 4740. In database D, an object is represented in changingpositions at trajectory 4750, with the reaction of objects representedby at 4745 and 4755.

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. All publications, patents, and patentapplications cited herein are hereby incorporated by reference for allpurposes in their entirety.

1. An adaptive dynamic computer system architecture having a pluralityof system layers interconnected to one another, comprising: A firstlayer including a hardware system including microprocessors, applicationspecific integrated circuits or continuously programmable fieldprogrammable gate arrays; A second layer including distributed nodes; Athird layer including distributed transformational spatio-temporalobject relational database management system; A fourth layer including amulti agent system of intelligent mobile software agents; A fifth layerincluding plasticity behavior in intrasystemic interaction; A sixthlayer including plasticity behavior in environmental interaction; Aseventh layer including a plurality of functional applications.